args.transfer_learning = False
else:
args.transfer_learning = False
if args.doc_classifier == 'Probabilistic':
#no doc loss for probabilitic model
args.coeff_gen_losses[1] = 0
args.coeff_model_losses[0] = 0
args.coeff_model_losses[1] = 0
args.coeff_model_losses[4] = 0
if args.need_preprocessing:
Preprocesseur = Preprocessor(args)
Preprocesseur.tokenize()
Preprocesseur.format_to_lines()
Preprocesseur.format_to_nn()
del Preprocesseur
gc.collect()
if args.mode == 'train' and not args.only_preprocessing:
train_ext(args, device_id)
elif args.mode == 'test':
checkpoint = args.load_model
# Print or save summaries and probas for test mode
logging.info("Processing files...")
with open('../results/patents_analysis.csv', mode='w') as file:
writer = csv.writer(file,
delimiter='?',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
